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MR-Linac Dosimetry: Current Approaches and Challenges

2023· article· en· W4388698834 on OpenAlexaff
Arman Sarfehnia

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsToronto Metropolitan UniversityCollège de MaisonneuveUniversity of Toronto
Fundersnot available
KeywordsDosimetryLinear particle acceleratorMedical physicsRadiation therapyNuclear medicineComputer sciencePhysicsMedicineOpticsRadiologyBeam (structure)

Abstract

fetched live from OpenAlex

Abstract The use of MR-integrated linacs (MR-linac) in clinical radiation oncology applications are becoming more prevalent. However, given the novel and radically different designs of these systems from conventional linacs, current radiation dosimetry protocols for high energy photons are no longer appropriate for use in modern MR-linac systems: First, given the presence of the magnetic field and the linac design, traditional reference conditions defined by previous high energy photon dosimetry protocols cannot be met; Second, the presence of the strong magnetic field can affect the performance of conventional equipment used for dosimetry. In this manuscript, we describe some of the challenges faced in radiation dosimetry in external MR-guided radiotherapy delivery systems, summarize some of the publications in this area, and finally discuss the early work by the American Association of physicists in Medicine (AAPM) Task Group 351 which is mandated with producing a protocol for reference dosimetry in MR-linac units.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.005
Scholarly communication0.0070.007
Open science0.0070.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.092
GPT teacher head0.302
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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